Job
Description
job requisition idJR1027352 Job Summary Synechron is seeking an analytical and innovative Senior Data Scientist to support and advance our data-driven initiatives. The ideal candidate will have a solid understanding of data science principles, hands-on experience with AI/ML tools and techniques, and the ability to interpret complex data sets to deliver actionable insights. This role contributes to the organizations strategic decision-making and technology innovation by applying advanced analytics and machine learning models in a collaborative environment. Software Required Skills: Python (including libraries such as pandas, scikit-learn, TensorFlow, PyTorch) proficiency in developing and deploying models R (optional, but preferred) Data management tools (SQL, NoSQL databases) Cloud platforms (preferably AWS or Azure) for data storage and ML deployment Jupyter Notebooks or similar interactive development environments Version control tools such as Git Preferred Skills: Big data technologies (Spark, Hadoop) Model deployment tools (MLflow, Docker, Kubernetes) Data visualization tools (Tableau, Power BI) Overall Responsibilities Analyze and interpret large and complex data sets to generate insights for business and technology initiatives. Assist in designing, developing, and implementing AI/ML models and algorithms to solve real-world problems. Collaborate with cross-functional teams including data engineers, software developers, and business analysts to integrate models into production systems. Stay current with emerging trends, research, and best practices in AI/ML/Data Science and apply them to ongoing projects. Document methodologies, modeling approaches, and insights clearly for technical and non-technical stakeholders. Support model validation, testing, and performance monitoring to ensure accuracy and reliability. Contribute to the development of data science workflows and standards within the organization. Performance Outcomes: Accurate and reliable data models that support strategic decision-making. Clear documentation and communication of findings and recommendations. Effective collaboration with technical teams to deploy scalable models. Continuous adoption of best practices in AI/ML and data management. Technical Skills (By Category) Programming Languages: Essential: Python (best practices in ML development), SQL Preferred: R, Java (for integration purposes) Databases/Data Management: SQL databases, NoSQL (MongoDB, Cassandra) Cloud data storage solutions (AWS S3, Azure Blob Storage) Cloud Technologies: AWS (S3, EC2, SageMaker, Lambda) Azure Machine Learning (preferred) Frameworks & Libraries: TensorFlow, PyTorch, scikit-learn, Keras, XGBoost Development Tools & Methodologies: Jupyter Notebooks, Git, CI/CD pipelines Agile and Scrum processes Security Protocols: Best practices in data security and privacy, GDPR compliance Experience 8+ years of professional experience in AI, ML, or Data Science roles. Proven hands-on experience designing and deploying ML models in real-world scenarios. Demonstrated ability to analyze complex data sets and translate findings into business insights. Previous experience working with cloud-based data science solutions is preferred. Strong portfolio showcasing data science projects, models developed, and practical impact. Alternative Pathways: Candidates with extensive research or academic experience in AI/ML can be considered, provided they demonstrate practical application of skills. Day-to-Day Activities Conduct data exploration, cleaning, feature engineering, and model development. Collaborate with data engineers to prepare data pipelines for model training. Build, validate, and refine machine learning models. Present insights, models, and recommendations to technical and business stakeholders. Support deployment of models into production environments. Monitor model performance and iterate to improve effectiveness. Participate in team meetings, project planning, and reviewing progress. Document methodologies and maintain version control of codebase. Qualifications Bachelors degree in Computer Science, Mathematics, Statistics, Data Science, or a related field; Masters or PhD highly desirable. Evidence of relevant coursework, certifications, or professional training in AI/ML. Professional certifications (e.g., AWS Certified Machine Learning Specialty, Microsoft Certified Data Scientist) are a plus. Commitment to ongoing professional development in AI/ML methodologies. Professional Competencies Strong analytical and critical thinking to solve complex problems. Effective communication skills for technical and non-technical audiences. Demonstrated ability to work collaboratively in diverse teams. Aptitude for learning new tools, techniques, and technologies rapidly. Innovation mindset with a focus on applying emerging research. Strong organizational skills to manage multiple projects and priorities.
